Create and Query a Time Series Collection

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Create a Time Series Collection

Insert Measurements into a Time Series Collection
Query a Time Series Collection
Run Aggregations on a Time Series Collection

This page shows how to create and query a time series collection, with code examples.

Important

Feature Compatibility Version Requirement

You can only create time series collections on a system with featureCompatibilityVersion set to 5.0 or greater.

Create a Time Series Collection

Define the timeField as the field that contains time data and the metaField as the field that contains metadata:

{
   timeField: "timestamp",
   metaField: "metadata"
}

In this example, timestamp is the name of the timeField and metadata is the name of the metaField. The value of the timestamp field must be a date type.

Important

Choosing the right metaField for your collection optimizes both storage and query performance. For more information on metaField selection and best practices, see metaFields.

Define the time interval for each bucket of data using one of the two approaches below:

Important

Changing Time Series Intervals

After creation, you can modify granularity or bucket definitions using the collMod method. However, you can only increase the time span covered by each bucket. You cannot decrease it.

Define a granularity field:
```
{
   granularity: "seconds"
}
```
For more detailed information on selecting a granularity value, see Granularity Considerations.

In MongoDB 6.3 and higher, you can define bucketMaxSpanSeconds and bucketRoundingSeconds fields. Both values must be the same:
```
{
   bucketMaxSpanSeconds: "300",
   bucketRoundingSeconds: "300"
}
```

Optionally, set expireAfterSeconds to expire documents when the value of the timeField is at least that old:

{
   expireAfterSeconds: 86400
}

Create the collection using either the db.createCollection() method or the create command. The follow example uses the db.createCollection() method to create a weather time series collection:

db.createCollection(
   "weather",
   {
      timeseries: {
         timeField: "timestamp",
         metaField: "metadata",
         granularity: "seconds"
      },
      expireAfterSeconds: 86400
   }
)

Time Series Field Reference

A time series collection includes the following fields:

Field	Type	Description
`timeseries.timeField`	string	Required. The name of the field which contains the date in each time series document. Documents in a time series collection must have a valid BSON date as the value for the `timeField`.
`timeseries.metaField`	string	Optional. The name of the field which contains metadata in each time series document. The metadata in the specified field should be data that is used to label a unique series of documents. The metadata should rarely, if ever, change The name of the specified field may not be `_id` or the same as the `timeseries.timeField`. The field can be of any data type. Although the `metaField` field is optional, using metadata can improve query optimization. For example, MongoDB automatically creates a compound index on the `metaField` and `timeField` fields for new collections. If you do not provide a value for this field, the data is bucketed solely based on time.
`timeseries.granularity`	integer	Optional. Do not use if setting `bucketRoundingSeconds` and `bucketMaxSpanSeconds`. Possible values are `seconds` (default), `minutes`, and `hours`. Set `granularity` to the value that most closely matches the time between consecutive incoming timestamps. This improves performance by optimizing how MongoDB stores data in the collection. For more information on granularity and bucket intervals, see Set Granularity for Time Series Data.
`timeseries.bucketMaxSpanSeconds`	integer	Optional. Use with `bucketRoundingSeconds` as an alternative to `granularity`. Sets the maximum time between timestamps in the same bucket. Possible values are 1-31536000. New in version 6.3.
`timeseries.bucketRoundingSeconds`	integer	Optional. Use with `bucketMaxSpanSeconds` as an alternative to `granularity`. Must be equal to `bucketMaxSpanSeconds`. When a document requires a new bucket, MongoDB rounds down the document's timestamp value by this interval to set the minimum time for the bucket. New in version 6.3.
`expireAfterSeconds`	integer	Optional. Enable the automatic deletion of documents in a time series collection by specifying the number of seconds after which documents expire. MongoDB deletes expired documents automatically. See Set up Automatic Removal for Time Series Collections (TTL) for more information.

Other allowed options that are not specific to time series collections are:

storageEngine
indexOptionDefaults
collation
writeConcern
comment

Tip

See:

db.createCollection() and create.

Insert Measurements into a Time Series Collection

Each document you insert should contain a single measurement. To insert multiple documents at once, issue the following command:

db.weather.insertMany( [
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-18T00:00:00.000Z"),
      temp: 12
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-18T04:00:00.000Z"),
      temp: 11
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-18T08:00:00.000Z"),
      temp: 11
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-18T12:00:00.000Z"),
      temp: 12
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-18T16:00:00.000Z"),
      temp: 16
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-18T20:00:00.000Z"),
      temp: 15
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-19T00:00:00.000Z"),
      temp: 13
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-19T04:00:00.000Z"),
      temp: 12
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-19T08:00:00.000Z"),
      temp: 11
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-19T12:00:00.000Z"),
      temp: 12
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-19T16:00:00.000Z"),
      temp: 17
   },
   {
      metadata: { sensorId: 5578, type: "temperature" },
      timestamp: ISODate("2021-05-19T20:00:00.000Z"),
      temp: 12
   }
] )

To insert a single document, use the db.collection.insertOne() method.

Tip

Optimize Insert Performance

To learn how to optimize inserts for large operations, see Inserts Best Practices.

Query a Time Series Collection

You query a time series collection the same way you query a standard MongoDB collection.

To return one document from a time series collection, run:

db.weather.findOne( {
   timestamp: ISODate("2021-05-18T00:00:00.000Z")
} )

Example output:

{
   timestamp: ISODate("2021-05-18T00:00:00.000Z"),
   metadata: { sensorId: 5578, type: 'temperature' },
   temp: 12,
   _id: ObjectId("62f11bbf1e52f124b84479ad")
}

For more information on time series queries, see Query Best Practices.

Run Aggregations on a Time Series Collection

For additional query functionality, use an aggregation pipeline such as:

db.weather.aggregate( [
   {
      $project: {
         date: {
            $dateToParts: { date: "$timestamp" }
         },
         temp: 1
      }
   },
   {
      $group: {
         _id: {
            date: {
               year: "$date.year",
               month: "$date.month",
               day: "$date.day"
            }
         },
         avgTmp: { $avg: "$temp" }
      }
   }
] )

The example aggregation pipeline groups all documents by the date of the measurement and then returns the average of all temperature measurements that day:

 {
  "_id" : {
    "date" : {
      "year" : 2021,
      "month" : 5,
      "day" : 18
    }
  },
  "avgTmp" : 12.714285714285714
}
{
  "_id" : {
    "date" : {
      "year" : 2021,
      "month" : 5,
      "day" : 19
    }
  },
  "avgTmp" : 13
}

Back

Aggregations and Operators

Time Series Indexes